Adaptive Algorithm Preventing Drug Adaptation in Chronic Treatment
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Solution Overview
Problem
Chronic disease treatments often face efficacy loss due to body adaptation, tolerance, and tachyphylaxis, leading to reduced effectiveness over time, as the body adapts to prolonged drug exposure, resulting in decreased response rates and increased side effects.
Innovation Solution
A subject-specific, disease-tailored algorithm is developed using machine learning to optimize drug administration schedules, dosages, and combinations, incorporating adjuvant medications that target microtubules or glycosphingolipid pathways, to prevent adaptation and enhance treatment efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed treatment protocol is used for chronic diseases, then the treatment is simple to implement and follow, but the body adapts to the treatment over time causing loss of efficacy
Solution Approach 1:
The treatment protocol transitions from a static, fixed regimen to a dynamic, adaptive regimen that automatically adjusts dosing intervals and amounts based on real-time physiological data. The system continuously modifies treatment parameters to prevent body adaptation while maintaining ease of operation through automated adjustment.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring physiological parameters (glucose levels, insulin levels, metabolic markers) and using this information to adjust treatment protocols. This feedback mechanism prevents loss of efficacy by detecting adaptation early and modifying the treatment accordingly.
2Reliability
If drug dosage is increased to overcome tolerance, then the drug effect is temporarily amplified, but tolerance accelerates and side effects increase
Solution Approach 1:
Instead of uniformly increasing dosage, the system applies partial actions by selectively adjusting doses based on real-time physiological needs. The adaptive algorithm determines the minimum necessary dosage to achieve therapeutic effect, avoiding excessive dosing that would accelerate tolerance and increase side effects.
Solution Approach 2:
The system changes multiple parameters simultaneously (dosage amount, dosing interval, administration timing) rather than simply increasing dose. This multi-parameter adjustment optimizes drug effect while minimizing tolerance development and side effects by maintaining drug levels within the therapeutic window more effectively.
3Reliability
If treatment protocol is customized for each subject to prevent adaptation, then treatment efficacy is maintained long-term, but the system complexity increases
Solution Approach 1:
The treatment system becomes self-adjusting by automatically monitoring physiological parameters and modifying protocols without requiring manual intervention. The system serves itself by using its own collected data to optimize treatment, reducing the complexity burden on users while maintaining personalized effectiveness.
Solution Approach 2:
The adaptive treatment platform serves multiple functions: it monitors physiological parameters, analyzes adaptation patterns, calculates optimal dosing regimens, and adjusts treatment protocols. This multi-functionality is achieved through an integrated system that combines sensing, processing, and control capabilities in a unified platform.
4Reliability
If adjuvant medications targeting microtubules or glycosphingolipid pathways are added, then adaptation and tolerance are reduced, but the treatment regimen becomes more complex
Solution Approach 1:
The system merges the primary medication with adjuvant medications targeting microtubules or glycosphingolipid pathways into an integrated treatment regimen. The adaptive algorithm coordinates dosing schedules and amounts of all medications to achieve synergistic effects that prevent adaptation while managing overall regimen complexity through unified control.
Data Source
AI summary
There are provided herein a system and a computer implemented method for preventing, mitigating or treating partial/complete loss of effect of one or more drugs or medical devices administered to or used by a subject in need thereof due to adaptation, tolerance, and/or tachyphylaxis, and/or for preventing, mitigating or treating non-responsiveness to one or more drugs, maximizing therapeutic effect of one or more drugs, or for improving target or non-target organ/organs response to therapy, the system/method include (processing circuit configured to): receiving a plurality of physiological or pathological parameters of the subject; applying a machine learning algorithm on the plurality of physiological or pathological parameters; and determining a subject-specific administration regimen of a drug or a medical treatment, wherein the administration regimen comprises drug administration parameters, cell/tissue/organ stimulation parameters, adjuvant parameters or any combination thereof; wherein the administration regimen is irregular.